Introduction to single-cell RNA-seq analysis
Quality Control
- 1 Introduction
- 2 Load packages
- 3 Reading CellRanger output into R
- 4 Properties of scRNA-seq data
- 5 Quality control
- 6 QC and filtering by combining the metrics
- 7 Mitochondrial content versus library size
1 Introduction
Now that the read alignment and quantification have been carried out using cellranger (v7.0.0), we will begin by assessing the data quality and applying further filtering of cells to remove poor quality data.
2 Load packages
library(DropletUtils)
library(scater)
library(ensembldb)
library(AnnotationHub)
library(BiocParallel)
library(tidyverse)
library(patchwork)
library(ggvenn)- DropletUtils - utilities for the analysis of droplet-based data
- scater - single cell QC and analysis tools
- ensembldb & AnnotationHub - for gene annotation
- BiocParallel - for parallelisation of some functions
- tidyverse, patchwork & ggvenn - for data.frame manipulation and plotting
3 Reading CellRanger output into R
3.1 Sample meta data
We have already prepared a sample meta data table that relates the SRA sample ID to the sample group. The table below shows details of the twelve samples that we will be working with.
samplesheet <- read_tsv("sample_sheet.tsv")3.1.1 Parallelisation
scRNAseq data sets tend to be very large and processing them requires a large amount of computing power and can take time. Many of the commands we will use have the option to be run in parallel across multiple processors. By default they will only use a single processor, but parallelisation will greatly speed up the analysis.
We need to first set up some parallel parameters using the package BiocParallel.
bp.params <- MulticoreParam(workers = 7)Here were are selecting to use forked processes with
MulticoreParam and instructing the function to use 7 cores
(our machines have 8, this leaves 1 to run the desktop etc.). Note that
on Windows MulticoreParam does not work and it is necessary
to use SnowParam - please refer to the
BiocParallel vignettes for further information.
3.1.2 Loading a single sample
We will load the data for the SRR9264343. To load
the data from the CellRanger outputs, we need to use the function
read10xCounts from the DropletUtils package.
We pass the function the location of the directory containing the counts
matrix, cell barcodes and features (genes).
We could load the raw data, which includes counts for all
cell barcodes detected in the sample, and use the
emptyDrops function in DropletUtils to call cells, however,
CellRanger has already called cells and so we are going to work with the
filtered matrix, which only contains droplets called as cells
by CellRanger.
sample.path <- "SRR9264343/outs/filtered_feature_bc_matrix/"
sce.sing <- read10xCounts(sample.path, col.names=TRUE, BPPARAM = bp.params)
sce.sing## class: SingleCellExperiment
## dim: 36601 3094
## metadata(1): Samples
## assays(1): counts
## rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
## ENSG00000277196
## rowData names(3): ID Symbol Type
## colnames(3094): AAACCTGAGACTTTCG-1 AAACCTGGTCTTCAAG-1 ...
## TTTGTCACAGGCTCAC-1 TTTGTCAGTTCGGCAC-1
## colData names(2): Sample Barcode
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):3.1.3 The
SingleCellExperiment object
The data have been loaded as a SingleCellExperiment object. The details of the structure of the object are described here. In summary, it stores various data types in a single object. Currently it will contain:
- the count matrix
- feature (gene) metadata
- cell (droplet) metadata
Later we will also add the outcomes of downstream analysis such as normalisation and dimensionality reduction.
3.1.4 The counts matrix
Compared to bulk RNA-seq, Single-cell RNA-seq data is sparse, i.e. there many missing values or zeroes. This is particularly true with droplet-based methods such as 10X, mostly because:
- any given cell does not express each gene
- the library preparation does not capture all transcript the cell does express
- the sequencing depth per cell is far lower and so fewer of the expressed genes are detected
We can access the counts matrix with counts. Given the
large number of droplets in a sample, count matrices can be large.
dim(counts(sce.sing))## [1] 36601 3094They are however very sparse, that is, most of the entries are 0’s.
To save memory the counts can be stored in a ‘sparse matrix’ that only
stores non-zero values, in this case as a dgCMatrix
object.
counts(sce.sing)[1:10, 1:10]## 10 x 10 sparse Matrix of class "dgCMatrix"
##
## ENSG00000243485 . . . . . . . . . .
## ENSG00000237613 . . . . . . . . . .
## ENSG00000186092 . . . . . . . . . .
## ENSG00000238009 . . . . . . . . . .
## ENSG00000239945 . . . . . . . . . .
## ENSG00000239906 . . . . . . . . . .
## ENSG00000241860 . . . . . . . . . .
## ENSG00000241599 . . . . . . . . . .
## ENSG00000286448 . . . . . . . . . .
## ENSG00000236601 . . . . . . . . . .3.1.5 Features
Details about the “features” (in this case genes) can by accessed
using the rowData function. Currently it contains the
ensembl gene ID and the gene symbol, which have been derived from the
10x reference used by CellRanger. It also contains a “Type” column,
which tells us what sort of data we are looking at; in this case it is
“Gene Expression”. If we wish to, we can add further annotation to the
features by adding extra columns to this data frame.
rowData(sce.sing)## DataFrame with 36601 rows and 3 columns
## ID Symbol Type
## <character> <character> <character>
## ENSG00000243485 ENSG00000243485 MIR1302-2HG Gene Expression
## ENSG00000237613 ENSG00000237613 FAM138A Gene Expression
## ENSG00000186092 ENSG00000186092 OR4F5 Gene Expression
## ENSG00000238009 ENSG00000238009 AL627309.1 Gene Expression
## ENSG00000239945 ENSG00000239945 AL627309.3 Gene Expression
## ... ... ... ...
## ENSG00000277836 ENSG00000277836 AC141272.1 Gene Expression
## ENSG00000278633 ENSG00000278633 AC023491.2 Gene Expression
## ENSG00000276017 ENSG00000276017 AC007325.1 Gene Expression
## ENSG00000278817 ENSG00000278817 AC007325.4 Gene Expression
## ENSG00000277196 ENSG00000277196 AC007325.2 Gene ExpressionThe rows of this table correspond to the rows of the count matrix; the row names of this table will match the row names of the counts matrix - currently these are the Ensembl IDS:
rownames(counts(sce.sing))[1:6]## [1] "ENSG00000243485" "ENSG00000237613" "ENSG00000186092" "ENSG00000238009"
## [5] "ENSG00000239945" "ENSG00000239906"3.1.6 Droplet annotation
Details about the droplets can be accessed using
colData. Currently it contains the sample names and droplet
Barcodes. As with the feature data, we can add additional information
about each droplet, e.g. counts of genes or the percentage of
mitochondrial genes, to this data frame. The rows of this table
correspond to the data in the columns of the count matrix; the row names
of this table will match the column names of the counts matrix -
currently these are the droplet barcodes.
colData(sce.sing)## DataFrame with 3094 rows and 2 columns
## Sample Barcode
## <character> <character>
## AAACCTGAGACTTTCG-1 SRR9264343/outs/filt.. AAACCTGAGACTTTCG-1
## AAACCTGGTCTTCAAG-1 SRR9264343/outs/filt.. AAACCTGGTCTTCAAG-1
## AAACCTGGTGCAACTT-1 SRR9264343/outs/filt.. AAACCTGGTGCAACTT-1
## AAACCTGGTGTTGAGG-1 SRR9264343/outs/filt.. AAACCTGGTGTTGAGG-1
## AAACCTGTCCCAAGTA-1 SRR9264343/outs/filt.. AAACCTGTCCCAAGTA-1
## ... ... ...
## TTTGGTTTCTTTAGGG-1 SRR9264343/outs/filt.. TTTGGTTTCTTTAGGG-1
## TTTGTCAAGAAACGAG-1 SRR9264343/outs/filt.. TTTGTCAAGAAACGAG-1
## TTTGTCAAGGACGAAA-1 SRR9264343/outs/filt.. TTTGTCAAGGACGAAA-1
## TTTGTCACAGGCTCAC-1 SRR9264343/outs/filt.. TTTGTCACAGGCTCAC-1
## TTTGTCAGTTCGGCAC-1 SRR9264343/outs/filt.. TTTGTCAGTTCGGCAC-1The rows of this table correspond to the data in the columns of the count matrix; the row names of this table will match the column names of the counts matrix - currently these are the droplet barcodes:
colnames(counts(sce.sing))[1:6]## [1] "AAACCTGAGACTTTCG-1" "AAACCTGGTCTTCAAG-1" "AAACCTGGTGCAACTT-1"
## [4] "AAACCTGGTGTTGAGG-1" "AAACCTGTCCCAAGTA-1" "AAACCTGTCGAATGCT-1"4 Properties of scRNA-seq data
4.1 Number of genes detected per cell
The number and identity of genes detected in a cell varies greatly across cells: the total number of genes detected across all cells is far larger than the number of genes detected in each cell.
For the current set of samples the total number of genes detected across cells was 19938 out of 36601 gene in the reference, but if we look at the number of genes detected in each cell, we can see that this ranges from 35 to 4131, with a median of 1771.
genesPerCell <- colSums(counts(sce.sing) > 0)
plot(density(genesPerCell), main="", xlab="Genes per cell")
4.2 Total UMI for a gene versus the number of times detected
If we compare the number of UMI’s assigned to an individual gene to the number of cells in which that gene is detected, we can see that highly expressed genes tend to be detected in a higher proportion of cells than lowly expressed genes.
plot(rowSums(counts(sce.sing)) / rowSums(counts(sce.sing) > 0),
rowMeans(counts(sce.sing) > 0),
log = "x",
xlab="Mean UMIs per cell",
ylab="proportion of cells expressing the gene"
)
4.3 Distribution of counts for a gene across cells
We could also look at the distribution of counts for individual genes across all cells. The plot below shows this distribution for the top 20 genes detected.
rel_expression <- t( t(counts(sce.sing)) / colSums(counts(sce.sing))) * 100
rownames(rel_expression) <- rowData(sce.sing)$Symbol
most_expressed <- sort(rowSums( rel_expression ), decreasing = T)[20:1]
plot_data <- as.matrix(t(rel_expression[names(most_expressed),]))
boxplot(plot_data, cex=0.1, las=1, xlab="% total count per cell", horizontal=TRUE)
5 Quality control
The cell calling performed by CellRanger does not always retain only droplets containing cells. Poor-quality cells, or rather droplets, may be caused by cell damage during dissociation or failed library preparation. They usually have low UMI counts, few genes detected and/or high mitochondrial content. The presence of these droplets in the data set may affect normalisation, assessment of cell population heterogeneity, and clustering:
- Normalisation: Contaminating genes, ‘the ambient RNA’, are detected
at low levels in all libraries. In low quality libraries with low RNA
content, scaling will increase counts for these genes more than for
better-quality cells, resulting in their apparent upregulation in these
cells and increased variance overall.
- Cell population heterogeneity: variance estimation and
dimensionality reduction with PCA where the first principal component
will be correlated with library size, rather than biology.
- Clustering: higher mitochondrial and/or nuclear RNA content may cause low-quality cells to cluster separately or form states or trajectories between distinct cell types.
In order to remove or reduce the impact of poor-quality droplets on our downstream analysis we will attempt to filter them out using some QC metrics. The three principle means of doing this are to apply thresholds for inclusion on three characteristics:
The library size defined as the total sum of UMI counts across all genes; cells with small library sizes are considered to be of low quality as the RNA has not been efficiently captured, i.e. converted into cDNA and amplified, during library preparation.
The number of expressed genes in each cell defined as the number of genes with non-zero counts for that cell; any cell with very few expressed genes is likely to be of poor quality as the diverse transcript population has not been successfully captured.
The proportion of UMIs mapped to genes in the mitochondrial genome; high proportions are indicative of poor-quality cells, possibly because of loss of cytoplasmic RNA from perforated cells (the reasoning is that mitochondria are larger than individual transcript molecules and less likely to escape through tears in the cell membrane).
The scater
function addPerCellQC() will compute various per droplet QC
metrics and will add this information as new columns in the droplet
annotation (colData) of the single cell object.
5.1 Load multiple samples
We can load multiple samples at the same time using the
read10xCounts command. This will create a single object
containing the data for multiple samples. We can then QC and filter the
samples in conjunction. As we will see later, this is not always optimal
when samples have been processed in multiple batches.
As an example we will one sample from each sample group. Again we
will start with the filtered counts matrix, which only contains cells
called by CellRanger. We pass the read10xCounts a named
vector containing the paths to the filtered counts matrices that we wish
to load; the names of the vector will be used as the sample names in the
Single Cell Experiment object.
samples <- samplesheet$Sample[c(1,5,7,9)]
list_of_files <- str_c("CellRanger_Outputs/CaronBourque2020/",
samples,
"/outs/filtered_feature_bc_matrix")
names(list_of_files) <- samples
list_of_files## SRR9264343
## "CellRanger_Outputs/CaronBourque2020/SRR9264343/outs/filtered_feature_bc_matrix"
## SRR9264347
## "CellRanger_Outputs/CaronBourque2020/SRR9264347/outs/filtered_feature_bc_matrix"
## SRR9264349
## "CellRanger_Outputs/CaronBourque2020/SRR9264349/outs/filtered_feature_bc_matrix"
## SRR9264351
## "CellRanger_Outputs/CaronBourque2020/SRR9264351/outs/filtered_feature_bc_matrix"sce <- read10xCounts(list_of_files, col.names=TRUE, BPPARAM = bp.params)
sce## class: SingleCellExperiment
## dim: 36601 13721
## metadata(1): Samples
## assays(1): counts
## rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
## ENSG00000277196
## rowData names(3): ID Symbol Type
## colnames(13721): 1_AAACCTGAGACTTTCG-1 1_AAACCTGGTCTTCAAG-1 ...
## 4_TTTGGTTGTGCATCTA-1 4_TTTGTCACAGCTCGAC-1
## colData names(2): Sample Barcode
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):5.2 Modify the droplet annotation
Currently, the droplet annotation in colData slot of the
sce object has two columns: “Sample” and “Barcode”. The
“Sample” is the name of the sample as we provided it to
read10xCounts, the “Barcode” is the barcode for the droplet
(cell).
colData(sce)## DataFrame with 13721 rows and 2 columns
## Sample Barcode
## <character> <character>
## 1_AAACCTGAGACTTTCG-1 SRR9264343 AAACCTGAGACTTTCG-1
## 1_AAACCTGGTCTTCAAG-1 SRR9264343 AAACCTGGTCTTCAAG-1
## 1_AAACCTGGTGCAACTT-1 SRR9264343 AAACCTGGTGCAACTT-1
## 1_AAACCTGGTGTTGAGG-1 SRR9264343 AAACCTGGTGTTGAGG-1
## 1_AAACCTGTCCCAAGTA-1 SRR9264343 AAACCTGTCCCAAGTA-1
## ... ... ...
## 4_TTTGCGCTCTTGTTTG-1 SRR9264351 TTTGCGCTCTTGTTTG-1
## 4_TTTGGTTAGATAGTCA-1 SRR9264351 TTTGGTTAGATAGTCA-1
## 4_TTTGGTTAGGTGCTAG-1 SRR9264351 TTTGGTTAGGTGCTAG-1
## 4_TTTGGTTGTGCATCTA-1 SRR9264351 TTTGGTTGTGCATCTA-1
## 4_TTTGTCACAGCTCGAC-1 SRR9264351 TTTGTCACAGCTCGAC-1The “Barcode” column contains the cell/droplet barcode and comprises the actual sequence and a ‘group ID’, e.g. AAACCTGAGAAACCAT-1. The ‘group ID’ helps distinguish cells from different samples that have identical barcode sequences, however, as each sample was processed separately with CellRanger, the group ID is set to 1 in all data sets. In the rownames DropUtils has helpfully add a prefix to each barcode to distinguish between samples. We will replace the “Barcode” column with the these.
We will also add information from the sample metadata table to the
colData object. We will be using the merge
function to do this. Unfortunately, this function removes the rownames
from the DFrame, so we will need to replace them.
sce$Barcode <- rownames(colData(sce))
colData(sce) <- merge(colData(sce), samplesheet, by="Sample", sort=FALSE)
rownames(colData(sce)) <- sce$Barcode5.3 Undetected genes
Although the count matrix has 36601 genes, many of these will not have been detected in any droplet.
detected_genes <- rowSums(counts(sce)) > 0
table(detected_genes)## detected_genes
## FALSE TRUE
## 11311 25290About a fifth of the genes have not been detected in any droplet. We can remove these before proceeding in order to reduce the size of the single cell experiment object.
sce <- sce[detected_genes,]5.4 Annotate genes
In order to assess the percentage of mitochondrial UMIs, we will need to be able to identify mitochondrial genes. The simplest way to do this is to annotate the genes with their chromosome of origin.
There are many ways we could annotate our genes in R. We will use
AnnotationHub. AnnotationHub has access to a large number
of annotation databases. Our genes are currently annotated with Ensembl
IDs, so we will use Ensembl human database. We will also specify that we
want the database corresponding to Ensembl release 107 as this the
release from which the CellRanger gene annotation was generated.
ah <- AnnotationHub()
ens.hs.107 <- query(ah, c("Homo sapiens", "EnsDb", 107))[[1]]
annotations <- genes(ens.hs.107, return.type = "data.frame") %>%
dplyr::select(gene_id, seq_name) %>%
dplyr::rename(ID=gene_id)
gene_metadata <- as.data.frame(rowData(sce)) %>%
mutate(ID = rownames(.)) %>%
left_join(annotations, by = "ID") %>%
dplyr::rename(Chromosome=seq_name)
rowData(sce) <- DataFrame(gene_metadata)
rownames(sce) <- uniquifyFeatureNames(rowData(sce)$ID,
rowData(sce)$Symbol)
rowData(sce)## DataFrame with 25290 rows and 4 columns
## ID Symbol Type Chromosome
## <character> <character> <character> <character>
## AL627309.1 ENSG00000238009 AL627309.1 Gene Expression 1
## AL627309.5 ENSG00000241860 AL627309.5 Gene Expression 1
## AL669831.2 ENSG00000229905 AL669831.2 Gene Expression 1
## LINC01409 ENSG00000237491 LINC01409 Gene Expression 1
## FAM87B ENSG00000177757 FAM87B Gene Expression 1
## ... ... ... ... ...
## AC233755.2 ENSG00000277856 AC233755.2 Gene Expression KI270726.1
## AC233755.1 ENSG00000275063 AC233755.1 Gene Expression KI270726.1
## AC171558.1 ENSG00000274792 AC171558.1 Gene Expression KI270727.1
## AC023491.2 ENSG00000278633 AC023491.2 Gene Expression KI270731.1
## AC007325.4 ENSG00000278817 AC007325.4 Gene Expression KI270734.15.5 Add per cell QC metrics
We can now add per cell QC metrics to the droplet annotation using
the function addPerCellQC. In order to get the metrics for
the subset of mitochondrial genes, we need to pass the function a vector
indicating which genes are mitochondrial.
is.mito <- which(rowData(sce)$Chromosome=="MT")
sce <- addPerCellQC(sce, subsets=list(Mito=is.mito), BPPARAM = bp.params)The function has added six columns to the droplet annotation:
- sum: total UMI count
- detected: number of features (genes) detected
- subsets_Mito_sum: number of UMIs mapped to mitochondrial transcripts
- subsets_Mito_detected: number of mitochondrial genes detected
- subsets_Mito_percent: percentage of UMIs mapped to mitochondrial transcripts
- total: also the total UMI count
We will use sum, detected, and subsets_Mito_percent to further filter the cells.
colData(sce)## DataFrame with 13721 rows and 10 columns
## Sample Barcode SampleName SampleGroup
## <character> <character> <character> <character>
## 1_AAACCTGAGACTTTCG-1 SRR9264343 1_AAACCTGAGACTTTCG-1 ETV6-RUNX1_1 ETV6-RUNX1
## 1_AAACCTGGTCTTCAAG-1 SRR9264343 1_AAACCTGGTCTTCAAG-1 ETV6-RUNX1_1 ETV6-RUNX1
## 1_AAACCTGGTGCAACTT-1 SRR9264343 1_AAACCTGGTGCAACTT-1 ETV6-RUNX1_1 ETV6-RUNX1
## 1_AAACCTGGTGTTGAGG-1 SRR9264343 1_AAACCTGGTGTTGAGG-1 ETV6-RUNX1_1 ETV6-RUNX1
## 1_AAACCTGTCCCAAGTA-1 SRR9264343 1_AAACCTGTCCCAAGTA-1 ETV6-RUNX1_1 ETV6-RUNX1
## ... ... ... ... ...
## 4_TTTGCGCTCTTGTTTG-1 SRR9264351 4_TTTGCGCTCTTGTTTG-1 PBMMC_1 PBMMC
## 4_TTTGGTTAGATAGTCA-1 SRR9264351 4_TTTGGTTAGATAGTCA-1 PBMMC_1 PBMMC
## 4_TTTGGTTAGGTGCTAG-1 SRR9264351 4_TTTGGTTAGGTGCTAG-1 PBMMC_1 PBMMC
## 4_TTTGGTTGTGCATCTA-1 SRR9264351 4_TTTGGTTGTGCATCTA-1 PBMMC_1 PBMMC
## 4_TTTGTCACAGCTCGAC-1 SRR9264351 4_TTTGTCACAGCTCGAC-1 PBMMC_1 PBMMC
## sum detected subsets_Mito_sum subsets_Mito_detected
## <numeric> <integer> <numeric> <integer>
## 1_AAACCTGAGACTTTCG-1 6677 2056 292 12
## 1_AAACCTGGTCTTCAAG-1 12064 3177 575 12
## 1_AAACCTGGTGCAACTT-1 843 363 428 11
## 1_AAACCTGGTGTTGAGG-1 8175 2570 429 12
## 1_AAACCTGTCCCAAGTA-1 8638 2389 526 13
## ... ... ... ... ...
## 4_TTTGCGCTCTTGTTTG-1 11741 2737 331 11
## 4_TTTGGTTAGATAGTCA-1 3161 1029 143 10
## 4_TTTGGTTAGGTGCTAG-1 5798 1549 287 12
## 4_TTTGGTTGTGCATCTA-1 1856 475 43 9
## 4_TTTGTCACAGCTCGAC-1 2810 849 118 11
## subsets_Mito_percent total
## <numeric> <numeric>
## 1_AAACCTGAGACTTTCG-1 4.37322 6677
## 1_AAACCTGGTCTTCAAG-1 4.76625 12064
## 1_AAACCTGGTGCAACTT-1 50.77106 843
## 1_AAACCTGGTGTTGAGG-1 5.24771 8175
## 1_AAACCTGTCCCAAGTA-1 6.08937 8638
## ... ... ...
## 4_TTTGCGCTCTTGTTTG-1 2.81918 11741
## 4_TTTGGTTAGATAGTCA-1 4.52388 3161
## 4_TTTGGTTAGGTGCTAG-1 4.94998 5798
## 4_TTTGGTTGTGCATCTA-1 2.31681 1856
## 4_TTTGTCACAGCTCGAC-1 4.19929 28105.6 QC metric distribution
Before moving on to do the actual cell filtering, it is always a good idea to explore the distribution of the metrics across the droplets.
We can use the scater function plotColData
to generate plots that provide a look at these distributions on a per
sample basis.
plotColData(sce, x="SampleName", y="sum") +
scale_y_log10() +
ggtitle("Total count")
plotColData(sce, x="SampleName", y="detected") +
scale_y_log10() +
ggtitle("Detected features")
plotColData(sce, x="SampleName", y="subsets_Mito_percent") +
ggtitle("Mito percent")
A scatter plot shows the extent to which library size and numbers of genes detected are correlated.
colData(sce) %>%
as.data.frame() %>%
arrange(subsets_Mito_percent) %>%
ggplot(aes(x = sum, y = detected)) +
geom_point(aes(colour = subsets_Mito_percent > 10)) +
facet_wrap(vars(SampleGroup))
5.7 Identification of low-quality cells with adaptive thresholds
One could use hard threshold for the library size, number of genes detected and mitochondrial content based on the distributions seen above. These would need vary across runs and the decision making process is somewhat arbitrary. It may therefore be preferable to rely on outlier detection to identify cells that markedly differ from most cells.
We saw above that the distribution of the QC metrics is close to Normal. Hence, we can detect outliers using the median and the median absolute deviation (MAD) from the median (not the mean and the standard deviation which both are sensitive to outliers).
For a given metric, an outlier value is one that lies over some number of MADs away from the median. A cell will be excluded if it is an outlier in the part of the range to avoid, for example low gene counts, or high mitochondrial content. For a normal distribution, a threshold defined with a distance of 3 MADs from the median retains about 99% of values.
The scater function isOutlier can be used
to detect outlier cells based on any metric in the colData
table. It returns a boolean vector that identifies outliers. By default
it will mark any cell that is 3 MADS in either direction from the median
as an outlier.
5.7.1 Library size
With library size we wish to identify outliers that have very low library sizes, this indicates that the droplets either contain poor quality cells, perhaps damaged or dying, or do not contain a cell at all.
The library size distribution tends to have a long tail to the right (small numbers of cells with very high UMI counts). We therefore log transform the library size in order to the make the distribution closer to normal. This also improves the resolution of the smaller library sizes and ensures that we do not end up with negative threshold.
low_lib_size <- isOutlier(sce$sum, log=TRUE, type="lower")
table(low_lib_size)## low_lib_size
## FALSE
## 13721This has excluded 0 cells. We can view the threshold values to check that they seem reasonable.
attr(low_lib_size, "thresholds")## lower higher
## 267.6521 InfWe can view the effect of the filtering using
plotColData.
colData(sce)$low_lib_size <- low_lib_size
plotColData(sce, x="SampleName", y="sum", colour_by = "low_lib_size") +
scale_y_log10() +
labs(y = "Total count", title = "Total count") +
guides(colour=guide_legend(title="Discarded"))
5.7.2 Number of genes
As with the library size, we will log tranform the number of genes detected prior to filtering using the median absolute deviation.
low_n_features <- isOutlier(sce$detected, log=TRUE, type="lower")
table(low_n_features)## low_n_features
## FALSE TRUE
## 13641 80This has excluded out 80 cells. The threshold value was:
attr(low_n_features, "thresholds")[1]## lower
## 208.4428We can view the effect of the filtering using
plotColData.
colData(sce)$low_n_features <- low_n_features
plotColData(sce, x="SampleName", y="detected", colour_by = "low_n_features") +
scale_y_log10() +
labs(y = "Genes detected", title = "Genes detected") +
guides(colour=guide_legend(title="Discarded"))
5.7.3 Mitochondrial content
For the mitochondrial content the exclusion zone is in the higher part of the distribution. For this reason we do not need to worry about log transforming the data as want to remove the long right hand tail anyway.
high_Mito_percent <- isOutlier(sce$subsets_Mito_percent, type="higher")
table(high_Mito_percent)## high_Mito_percent
## FALSE TRUE
## 11706 2015This has removed 2015 cells in total. The upper threshold value:
attr(high_Mito_percent, "thresholds")[2]## higher
## 14.44046We can view the effect of the filtering using
plotColData.
colData(sce)$high_Mito_percent <- high_Mito_percent
plotColData(sce,
x="SampleName",
y="subsets_Mito_percent",
colour_by = "high_Mito_percent") +
labs(y = "Percentage mitochondrial UMIs",
title = "Mitochondrial UMIs") +
guides(colour=guide_legend(title="Discarded"))
5.7.4 Summary of discarded cells
Having applied each of the three thresholds separately, we can now combine them to see how many droplets in total we will be excluding.
tibble(low_lib_size, low_n_features, high_Mito_percent) %>%
mutate(discard = low_lib_size | low_n_features | high_Mito_percent) %>%
mutate(SampleName=colData(sce)$SampleName) %>%
group_by(SampleName) %>%
summarise(across(where(is.logical), sum))## # A tibble: 4 × 5
## SampleName low_lib_size low_n_features high_Mito_percent discard
## <chr> <int> <int> <int> <int>
## 1 ETV6-RUNX1_1 0 8 285 291
## 2 HHD_1 0 25 1181 1185
## 3 PBMMC_1 0 12 22 29
## 4 PRE-T_1 0 35 527 5525.7.5 All three filter steps at once
The three steps above may be run in one go using the
quickPerCellQC function. This creates a DataFrame with 4
columns containing TRUE/FALSE - one for each filter metric and one
called “discard” that combined the three logicals.
cell_qc_results <- quickPerCellQC(colData(sce), sub.fields = TRUE)
cell_qc_results %>%
as.data.frame() %>%
mutate(SampleName=colData(sce)$SampleName) %>%
group_by(SampleName) %>%
summarise(across(where(is.logical), sum))## # A tibble: 4 × 5
## SampleName low_lib_size low_n_features high_subsets_Mito_percent discard
## <chr> <int> <int> <int> <int>
## 1 ETV6-RUNX1_1 0 8 285 291
## 2 HHD_1 0 25 1181 1185
## 3 PBMMC_1 0 12 22 29
## 4 PRE-T_1 0 35 527 5525.7.6 Assumptions
Data quality depends on the tissue analysed, some being difficult to dissociate, e.g. brain, so that one level of QC stringency will not fit all data sets.
Filtering based on QC metrics as done here assumes that these QC metrics are not correlated with biology. This may not necessarily be true in highly heterogenous data sets where some cell types represented by good-quality cells may have low RNA content or high mitochondrial content.
5.7.7 Considering experimental factors when filtering
The samples analysed here may have been processed in different
batches leading to differences in the overall distribution of UMI
counts, numbers of genes detected and mitochondrial content. Such
differences would affect the adaptive thesholds discussed above - that
is, as the distributions of the metrics differ, perhaps we should really
apply the adaptive thresholding for each batch rather than universally
across all samples. The quickPerCellQC has a “batch”
argument that allows us to specify with samples belong to which batches.
The batches are then filtered independently.
batch.cell_qc_results <- quickPerCellQC(colData(sce),
sub.fields = TRUE,
batch=sce$Sample)
batch.cell_qc_results %>%
as.data.frame() %>%
mutate(SampleName=colData(sce)$SampleName) %>%
group_by(SampleName) %>%
summarise(across(where(is.logical), sum))## # A tibble: 4 × 5
## SampleName low_lib_size low_n_features high_subsets_Mito_percent discard
## <chr> <int> <int> <int> <int>
## 1 ETV6-RUNX1_1 228 286 311 364
## 2 HHD_1 0 12 1052 1054
## 3 PBMMC_1 0 20 51 60
## 4 PRE-T_1 0 32 1025 1049The table below shows how the thresholds for each metric differ between the batch-wise analysis and the analysis using all samples.
all.thresholds <- tibble(`SampleName`="All",
`Library Size`=attr(cell_qc_results$low_lib_size, "thresholds")[1],
`Genes detected`=attr(cell_qc_results$low_n_features, "thresholds")[1],
`Mitochondrial UMIs`=attr(cell_qc_results$high_subsets_Mito_percent, "thresholds")[2])
tibble(`Sample`=names(attr(batch.cell_qc_results$low_lib_size, "thresholds")[1,]),
`Library Size`=attr(batch.cell_qc_results$low_lib_size, "thresholds")[1,],
`Genes detected`=attr(batch.cell_qc_results$low_n_features, "thresholds")[1,],
`Mitochondrial UMIs`=attr(batch.cell_qc_results$high_subsets_Mito_percent, "thresholds")[2,]) %>%
left_join(samplesheet) %>%
select(SampleName, `Library Size`, `Genes detected`, `Mitochondrial UMIs`) %>%
bind_rows(all.thresholds) %>%
mutate(across(where(is.numeric), round, digits=2)) %>%
datatable(rownames = FALSE, options = list(dom="t"))Let’s replace the columns in the droplet annotation with these new filters.
sce$low_lib_size <- batch.cell_qc_results$low_lib_size
sce$low_n_features <- batch.cell_qc_results$low_n_features
sce$high_Mito_percent <- batch.cell_qc_results$high_subsets_Mito_percent
sce$discard <- batch.cell_qc_results$discardWe can visualise how the new filters look using violin plots.
plotColData(sce, x="SampleName", y="sum", colour_by = "low_lib_size") +
scale_y_log10() +
labs(y = "Total count", title = "Total count") +
guides(colour=guide_legend(title="Discarded"))
plotColData(sce, x="SampleName", y="detected", colour_by = "low_n_features") +
scale_y_log10() +
labs(y = "Genes detected", title = "Genes detected") +
guides(colour=guide_legend(title="Discarded"))
plotColData(sce,
x="Sample",
y="subsets_Mito_percent",
colour_by = "high_Mito_percent") +
labs(y = "Percentage mitochondrial UMIs",
title = "Mitochondrial UMIs") +
guides(colour=guide_legend(title="Discarded"))
There are some distinct differences, most noticeable is that some cells are now being filtered based on library size for both ETV6-RUNX1_1 and PBMMC_1a. The venn diagrams below show how the number of discarded droplets in have changed for each filter in comparison to when the MAD filtering was applied across all samples.
pc1 <- tibble(`All together`=cell_qc_results$low_lib_size,
`By batch`=batch.cell_qc_results$low_lib_size) %>%
ggvenn(show_percentage = FALSE) +
labs(title="Library Size")
pc2 <- tibble(`All together`=cell_qc_results$low_n_features,
`By batch`=batch.cell_qc_results$low_n_features) %>%
ggvenn(show_percentage = FALSE) +
labs(title="Genes detected")
pc3 <- tibble(`All together`=cell_qc_results$high_subsets_Mito_percent,
`By batch`=batch.cell_qc_results$high_subsets_Mito_percent) %>%
ggvenn(show_percentage = FALSE) +
labs(title="Mitochondrial UMIs")
pc1 + pc2 + pc3
The most striking difference is in the filtering by library size. As we can see from the violin plots ETV6-RUNX1_1 has a markedly different library size distribution to the other three samples. When we applied the adaptive filters across all samples, the lower distributions of the other three samples caused the MADs to be distorted and resulted in a threshold that was inappropriately low for the ETV6-RUNX1_1 samples.
5.8 Filtering out poor quality cells
Now that we have identified poor quality cells we can filter them out before proceeding to do any further analysis.
sce.filtered <- sce[, !sce$discard]6 QC and filtering by combining the metrics
In the previous approach we used the three metrics in isolation to filter droplets. Another approach is to combine the three (or more) metrics in a single filtering step by looking for outliers in the multi-dimensional space defined by the metrics.
As with the adaptive thresholds above, this method should not be applied across batches or samples with differing distributions in the metrics or it will exclude many good quality cells. To demonstrate these methods, we’ll just extract one sample from our SingleCellExperiment object.
sce.E1 <- sce[ , sce$SampleName == "ETV6-RUNX1_1"]6.1 Using “outlyingness”
Essentially we need to reduce our 3 metrics to a single metric, we
can then use isOutlier to select outliers based on this
metric. One way to do this is to use the function
adjOutlyingness from the robustbase package.
This function computes the “outlyingness” for each droplet.
Here we will use the same three metrics as before: library size, the
number of genes detected and the mitochondrial content. Remember that
for “sum” (total UMIs) and “detected” (number of genes detected), we
want to use the log10 value.
library(robustbase)
stats <- cbind(log10(sce.E1$sum),
log10(sce.E1$detected),
sce.E1$subsets_Mito_percent)
outlying <- adjOutlyingness(stats, only.outlyingness = TRUE)
multi.outlier <- isOutlier(outlying, type = "higher")
summary(multi.outlier)## Mode FALSE TRUE
## logical 2877 2176.2 Using PCA
Another approach is to perform a principal component analysis (PCA)
on the table of metrics, apply adjOutlyingness to the
metrics table and use this to detect outliers. The scater
function runColDataPCA can be used to perform the PCA and
detect outliers. We’ll need to add a couple of columns to the
colData for the log10 metrics first.
sce.E1$log10sum <- log10(sce.E1$sum)
sce.E1$log10detected <- log10(sce.E1$detected)
sce.E1 <- runColDataPCA(sce.E1,
variables=list("log10sum",
"log10detected",
"subsets_Mito_percent"),
outliers=TRUE,
BPPARAM = bp.params)This has added the results of the principal component analysis into a
new slot in the SingleCellExperiment object specifically for holding the
results of dimension reduction transformations such as PCA, t-SNE and
UMAP. The results can be accessed using the reducedDim
function.
head(reducedDim(sce.E1))## PC1 PC2
## 1_AAACCTGAGACTTTCG-1 -0.8877986 0.05661503
## 1_AAACCTGGTCTTCAAG-1 -1.8334220 0.70109949
## 1_AAACCTGGTGCAACTT-1 5.9369782 2.99692981
## 1_AAACCTGGTGTTGAGG-1 -1.2410091 0.40653348
## 1_AAACCTGTCCCAAGTA-1 -1.1504532 0.48467810
## 1_AAACCTGTCGAATGCT-1 1.6947054 -1.22707540It has also added a column “outlier” to the colData,
which specifies the droplets that have been identified as outliers.
summary(sce.E1$outlier)## Mode FALSE TRUE
## logical 2873 2216.3 A note on multi-dimensional filtering
These types of approach can provide more power for detecting outliers as they are looking at patterns across multiple metrics, however, it can be difficult to interpret the reason why any particular droplet has been excluded.
7 Mitochondrial content versus library size
A useful diagnostic plot for assessing the impact of the filtering is to do a scatter plot of the mitochondrial content against the library size. We can overlay our final filter metric using the point colour.
plotColData(sce,
x="sum",
y="subsets_Mito_percent",
other_fields="SampleName",
colour_by="discard") +
facet_wrap(~SampleName, ncol=5, scale="free_x")
7.1 Session information
sessionInfo()## R version 4.3.3 (2024-02-29)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS/LAPACK: /opt/miniforge3/envs/R/lib/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] robustbase_0.99-2 ggvenn_0.1.10
## [3] patchwork_1.2.0 lubridate_1.9.3
## [5] forcats_1.0.0 stringr_1.5.1
## [7] dplyr_1.1.4 purrr_1.0.2
## [9] readr_2.1.5 tidyr_1.3.1
## [11] tibble_3.2.1 tidyverse_2.0.0
## [13] BiocParallel_1.36.0 AnnotationHub_3.10.0
## [15] BiocFileCache_2.10.1 dbplyr_2.4.0
## [17] ensembldb_2.26.0 AnnotationFilter_1.26.0
## [19] GenomicFeatures_1.54.1 AnnotationDbi_1.64.1
## [21] scater_1.30.1 ggplot2_3.5.0
## [23] scuttle_1.12.0 DropletUtils_1.22.0
## [25] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
## [27] Biobase_2.62.0 GenomicRanges_1.54.1
## [29] GenomeInfoDb_1.38.1 IRanges_2.36.0
## [31] S4Vectors_0.40.2 BiocGenerics_0.48.1
## [33] MatrixGenerics_1.14.0 matrixStats_1.2.0
## [35] knitr_1.45 DT_0.32
##
## loaded via a namespace (and not attached):
## [1] later_1.3.2 BiocIO_1.12.0
## [3] bitops_1.0-7 filelock_1.0.3
## [5] R.oo_1.26.0 XML_3.99-0.16.1
## [7] lifecycle_1.0.4 edgeR_4.0.2
## [9] lattice_0.22-5 vroom_1.6.5
## [11] crosstalk_1.2.1 magrittr_2.0.3
## [13] limma_3.58.1 sass_0.4.8
## [15] rmarkdown_2.26 jquerylib_0.1.4
## [17] yaml_2.3.8 httpuv_1.6.14
## [19] cowplot_1.1.3 DBI_1.2.2
## [21] abind_1.4-5 zlibbioc_1.48.0
## [23] R.utils_2.12.3 RCurl_1.98-1.14
## [25] rappdirs_0.3.3 GenomeInfoDbData_1.2.11
## [27] ggrepel_0.9.5 irlba_2.3.5.1
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## [31] codetools_0.2-19 DelayedArray_0.28.0
## [33] xml2_1.3.6 tidyselect_1.2.0
## [35] farver_2.1.1 ScaledMatrix_1.10.0
## [37] viridis_0.6.5 GenomicAlignments_1.38.0
## [39] jsonlite_1.8.8 BiocNeighbors_1.20.0
## [41] ellipsis_0.3.2 tools_4.3.3
## [43] progress_1.2.3 Rcpp_1.0.12
## [45] glue_1.7.0 gridExtra_2.3
## [47] SparseArray_1.2.2 xfun_0.42
## [49] HDF5Array_1.30.0 withr_3.0.0
## [51] BiocManager_1.30.22 fastmap_1.1.1
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## [57] timechange_0.3.0 R6_2.5.1
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